scripts/align_all_parallel.py

"""
brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
author: lzhbrian (https://lzhbrian.me)
date: 2020.1.5
note: code is heavily borrowed from 
    https://github.com/NVlabs/ffhq-dataset
    http://dlib.net/face_landmark_detection.py.html

requirements:
    apt install cmake
    conda install Pillow numpy scipy
    pip install dlib
    # download face landmark model from:
    # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
"""
from argparse import ArgumentParser
from utils.common_utils import AlignerCantFindFaceError
import time
import numpy as np
import PIL
import PIL.Image
import os
import scipy
import scipy.ndimage
import dlib
import multiprocessing as mp
import math

SHAPE_PREDICTOR_PATH = "pretrained_models/shape_predictor_68_face_landmarks.dat"


def get_landmark(filepath, predictor):
    """get landmark with dlib
    :return: np.array shape=(68, 2)
    """
    detector = dlib.get_frontal_face_detector()

    img = dlib.load_rgb_image(filepath)
    dets = detector(img, 1)

    if len(dets) < 1:
        raise AlignerCantFindFaceError("Face parser can not find face in your image :c Try to upload another one.")

    print(f"Found {len(dets)} faces, getting the largest")
    dets = sorted(dets, key=lambda det: det.width() * det.height(), reverse=True)
    shape = predictor(img, dets[0])

    t = list(shape.parts())
    a = []
    for tt in t:
        a.append([tt.x, tt.y])
    lm = np.array(a)
    return lm


def align_face(filepath, predictor):
    """
    :param filepath: str
    :return: PIL Image
    """
    unalign_dict = {}
    lm = get_landmark(filepath, predictor)
    print(f"Detected {len(lm)} facial landmarks")

    lm_chin = lm[0: 17]  # left-right
    lm_eyebrow_left = lm[17: 22]  # left-right
    lm_eyebrow_right = lm[22: 27]  # left-right
    lm_nose = lm[27: 31]  # top-down
    lm_nostrils = lm[31: 36]  # top-down
    lm_eye_left = lm[36: 42]  # left-clockwise
    lm_eye_right = lm[42: 48]  # left-clockwise
    lm_mouth_outer = lm[48: 60]  # left-clockwise
    lm_mouth_inner = lm[60: 68]  # left-clockwise

    # Calculate auxiliary vectors.
    eye_left = np.mean(lm_eye_left, axis=0)
    eye_right = np.mean(lm_eye_right, axis=0)
    eye_avg = (eye_left + eye_right) * 0.5
    eye_to_eye = eye_right - eye_left
    mouth_left = lm_mouth_outer[0]
    mouth_right = lm_mouth_outer[6]
    mouth_avg = (mouth_left + mouth_right) * 0.5
    eye_to_mouth = mouth_avg - eye_avg

    # Choose oriented crop rectangle.
    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
    x /= np.hypot(*x)
    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
    y = np.flipud(x) * [-1, 1]
    c = eye_avg + eye_to_mouth * 0.1
    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
    qsize = np.hypot(*x) * 2

    # read image
    img = PIL.Image.open(filepath)
    unalign_dict["orig_size"] = img.size

    output_size = 1024
    transform_size = 1024
    enable_padding = True

    # Shrink.
    shrink = int(np.floor(qsize / output_size * 0.5))
    if shrink > 1:
        rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
        unalign_dict["shrink"] = rsize
        img = img.resize(rsize, PIL.Image.ANTIALIAS)
        quad /= shrink
        qsize /= shrink

    # Crop.
    border = max(int(np.rint(qsize * 0.1)), 3)
    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
            int(np.ceil(max(quad[:, 1]))))
    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
            min(crop[3] + border, img.size[1]))
    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
        unalign_dict["crop"] = crop
        img = img.crop(crop)
        quad -= crop[0:2]

    # Pad.
    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
           int(np.ceil(max(quad[:, 1]))))
    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
           max(pad[3] - img.size[1] + border, 0))
    if enable_padding and max(pad) > border - 4:
        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
        unalign_dict["pad"] = pad
        img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
        h, w, _ = img.shape
        y, x, _ = np.ogrid[:h, :w, :1]
        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
                          1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
        blur = qsize * 0.02

        blur1 = (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
        img += blur1
        unalign_dict["blur1"] = blur1

        blur2 = (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
        img += blur2
        unalign_dict["blur2"] = blur2

        img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
        quad += pad[:2]

    # Transform.
    unalign_dict["pretrans_size"] = img.size
    unalign_dict["quad"] = quad
    img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
    if output_size < transform_size:
        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)

    # Save aligned image.
    return img, unalign_dict


def chunks(lst, n):
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        yield lst[i:i + n]


def extract_on_paths(file_paths):
    predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH)
    pid = mp.current_process().name
    print('\t{} is starting to extract on #{} images'.format(pid, len(file_paths)))
    tot_count = len(file_paths)
    count = 0
    for file_path, res_path in file_paths:
        count += 1
        if count % 100 == 0:
            print('{} done with {}/{}'.format(pid, count, tot_count))
        try:
            res = align_face(file_path, predictor)
            res = res.convert('RGB')
            os.makedirs(os.path.dirname(res_path), exist_ok=True)
            res.save(res_path)
        except Exception:
            continue
    print('\tDone!')


def parse_args():
    parser = ArgumentParser(add_help=False)
    parser.add_argument('--num_threads', type=int, default=1)
    parser.add_argument('--root_path', type=str, default='')
    args = parser.parse_args()
    return args


def run(args):
    root_path = args.root_path
    out_crops_path = root_path + '_crops'
    if not os.path.exists(out_crops_path):
        os.makedirs(out_crops_path, exist_ok=True)

    file_paths = []
    for root, dirs, files in os.walk(root_path):
        for file in files:
            file_path = os.path.join(root, file)
            fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path))
            res_path = '{}.jpg'.format(os.path.splitext(fname)[0])
            if os.path.splitext(file_path)[1] == '.txt' or os.path.exists(res_path):
                continue
            file_paths.append((file_path, res_path))

    file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads))))
    print(len(file_chunks))
    pool = mp.Pool(args.num_threads)
    print('Running on {} paths\nHere we goooo'.format(len(file_paths)))
    tic = time.time()
    pool.map(extract_on_paths, file_chunks)
    toc = time.time()
    print('Mischief managed in {}s'.format(toc - tic))


if __name__ == '__main__':
    args = parse_args()
    run(args)